Matthew Ziegler
2024-03-26
data %>% new_data %>% tables
data %>% different_data %>% figures
dat_cdiff <- read.csv("/Users/mattz/Documents/GitHub/shea24_demo/HAICViz_-_CDI_20240214.csv") %>%
janitor::clean_names() %>%
filter(topic =="Case rates (cases per 100,000)",
series =="Community-associated"|series=="Healthcare-associated")| year_name | topic | view_by | grouping | series | value |
|---|---|---|---|---|---|
| 2011 | Case rates (cases per 100,000) | Total | Epi Class | Community-associated | 48.16 |
| 2012 | Case rates (cases per 100,000) | Total | Epi Class | Community-associated | 52.88 |
| 2013 | Case rates (cases per 100,000) | Total | Epi Class | Community-associated | 55.75 |
| 2014 | Case rates (cases per 100,000) | Total | Epi Class | Community-associated | 57.83 |
| 2015 | Case rates (cases per 100,000) | Total | Epi Class | Community-associated | 65.81 |
| 2016 | Case rates (cases per 100,000) | Total | Epi Class | Community-associated | 67.20 |
dat_cdiff %>%
ggplot(aes(x = as.factor(year_name), y = value, fill= series)) +
geom_col(position = "dodge") +
labs(title = "Cases by year", y = "CDI cases per 1000 individuals", x = "Year")dat_cdiff_cat_plot <- read.csv("/Users/mattz/Documents/GitHub/shea24_demo/HAICViz_-_CDI_20240214.csv") %>%
janitor::clean_names() %>%
filter(topic =="Case rates (cases per 100,000)") %>%
mutate(cat = case_when(
grepl("HA|CA", series) ==TRUE & grepl("years", series) ==TRUE ~ "age",
grepl("Male|Female", series) ==TRUE & grepl("HA|CA", series) ==TRUE ~ "sex",
grepl("White|Non-white", series) ==TRUE & grepl("HA|CA", series) ==TRUE ~ "race")) %>%
filter(!is.na(cat)) %>%
separate(series, into = c("category","group"), sep =" - ") %>%
ggplot(aes(x = as.factor(year_name), y = value,
group = interaction(group, category),linetype = category, col = group,)) +
geom_line(lwd =1) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 75, vjust = 0, hjust=0)) +
facet_wrap(vars(cat)) +
labs(title = "C.difficile Infection by Year", y = "Case rates (cases per 100,000)", x = "Year") dat_mdrgn <- read.csv("/Users/mattz/Documents/GitHub/shea24_demo/HAICViz_-_MuGSI_20240330.csv") %>%
janitor::clean_names() %>%
mutate(keep = case_when(
viewby =="Organism" & series != "All cases" ~ 1,
organism =="CRAB" & viewby == "All cases" & topic =="Case Rates" ~ 1,
TRUE ~ 0
)) %>%
filter(keep ==1) %>%
mutate(series = ifelse(organism =="CRAB","Acinetobacter baumanii", series))dat_mdrgn_plot <- dat_mdrgn %>%
ggplot(aes(x = as.factor(year_name), y = value, group = series)) +
geom_line(aes(linetype = series)) +
theme(axis.text.x = element_text(angle = 75, vjust = 0, hjust=0)) +
labs(title = "Cases by year - Carbapenem-Resistant GNB",
y = "Cases per 1000 individuals", x = "Year") +
gghighlight(series =="Acinetobacter baumanii") +
theme_minimal()respi <- read.csv("/Users/mattz/Documents/GitHub/shea24_demo/Outpatient_Respiratory_Illness_Activity_Map_20240401.csv") %>%
janitor::clean_names() %>%
mutate(region = tolower(state)) %>%
separate(activity_level, into=c(NA, "level"), sep = " ") %>%
mutate(level = as.numeric(level)) %>%
filter(season =="2022-2023")
states <- map_data("state")gif_a <- region_dat_respi %>%
ggplot(., aes(long, lat, group = group)) +
geom_polygon(aes(fill = level),
colour = alpha("white", 1/2), size = 0.05) +
geom_polygon(data = states, colour = "black", fill = NA) +
scale_fill_gradientn(colours = terrain.colors(6)) +
theme_void() +
transition_time(week) +
labs(title = 'Respiratory Infection Activity 22-23 Season: Week {frame_time}') +
theme_minimal()
gif_a <- animate(gif_a, width = 700, height = 480)gif_b <- region_dat_respi %>%
#filter(!is.na(value)) %>%
ggplot(data = ., aes(y = level)) + geom_boxplot() +
labs(x = "", title = "National Value") +
theme(axis.text.x = element_blank()) +
transition_time(week)
#enter_fade() +
#exit_shrink() +
#ease_aes('sine-in-out')
gif_b <- animate(gif_b, width = 600, height = 480)vaccination <- read.csv("/Users/mattz/Documents/GitHub/shea24_demo/Vaccination_Coverage_among_Health_Care_Personnel_20240401.csv") %>%
janitor::clean_names() %>%
mutate(year = as.numeric(substr(season,1,4))) %>%
mutate(region = tolower(geography)) %>%
left_join(latitude_by_states, by = "region") %>%
rename(latitude = mean_lat) %>%
filter(personnel_type != "All Health Care Personnel")| Characteristic | Beta | 95% CI1 | p-value |
|---|---|---|---|
| year | 1.2 | 0.96, 1.5 | <0.001 |
| latitude | 0.34 | 0.22, 0.47 | <0.001 |
| personnel_type | |||
| Adult Students/Trainees and Volunteers | — | — | |
| Employees | 3.2 | 1.7, 4.6 | <0.001 |
| Licensed Independent Practitioners | -14 | -16, -13 | <0.001 |
| 1 CI = Confidence Interval | |||